The Data Storage and Memory Devices Group within the Seagate Research Group (SRG) is primarily responsible for performing experiments and simulations to guide the design the future of hard-disk drive based, and other, advanced data storage and memory technologies.
The work performed by this team directly informs the Company’s product roadmaps. Modeling and simulation are used to evaluate designs up to 10 years in the future, as well as elucidate experimentally observed phenomena. Machine learning models are also being developed to predict product reliability and inform future materials and design directions.
About the role - you will:
- Use materials science experience to innovate novel magnetic, plasmonic, electronic, and photonic nanomaterials
- Develop state-of-the-art AI/ML and data science solutions to solve/optimize technical problems in materials discovery or other advanced technology areas
- Leverage DFT and other modelling techniques to generate large datasets
- Explore the literature to propose novel materials or machine learning architectures
- Utilize big data analytics tools and apply analytical techniques for data retrieval, preparation, and discovery
About you:
- Passionate about materials science and data science
- Self-motivated, independent, and a team player with strong communication and interpersonal skills
- Innovative with a growth mindset, willing to learn new concepts and comfortable working outside your comfort zone
Your experience includes:
- Knowledge of fundamental materials science concepts
- Programming experience in Python
- Experience doing DFT simulations
- Good understanding and experience in machine learning and deep learning, e.g., DNN, CNN, Transformer, Reinforcement Learning, Active Learning, GAN, etc.
- Pursuing a PhD degree in Materials Science, Chemistry, Physics, Computer Engineering, Electrical engineering, Computer Science, Data Science, Math, or other related areas. Must be enrolled in Fall 2026 classes at a US university.
